学术报告报告题目：Single-cell entropy to quantify the cellular order parameter from single-cell RNA-seq data
报告时间：2019年11月5日（星期二）下午16: 00-17: 00
报 告 人：雷锦誌 教授（清华大学）
报告地点：4号楼4318室
邀 请 人：刘锐 教授
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数学学院
2019年11月1日
报告摘要：
The cell is the basic functional and biological unit of life, and a complex system contains a huge number of molecular components. How can we quantify the macroscopic state of a cell from the microscopic information of these molecular components? This is a fundamental question for better understanding of our own body. The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has allowed researches to look into the information of transcriptomes of individual cells. Despite considerable progress has been made in terms of cell type clustering on the past few years, these is no strong consensus about how can we define a cell state from scRNA-seq data. Here, we present single-cell entropy (scEntropy) as the order parameter for cellular transcriptome profile from single-cell RNA-seq data. scEntropy is straightforward to define the intrinsic transcriptional state of a cells, which can provide a quantity to measure the developmental process, and to distinguish different cell types. The proposed scEntropy followed by Gaussian mixture model (scEGMM) provides a coherent method of cell type classification, which is simple and includes no parameters and no clustering, and is comparable to existing machine learning-based methods in benchmarking studies. The results of cell type classification based on scEGMM are robust, and easy for biological interpretation.